1. Technical Field
The present disclosure relates to anatomy detection and, more specifically, to a system and method for transformation invariant landmark detection for anatomical primitives.
2. Discussion of Related Art
Anatomical primitives such as points, planes, and regions of interest can be an integral part of medical imaging analysis algorithms, such as tracking, registration, segmentation, detection, and recognition. In many conventional detection approaches, these primitives are manually detected and labeled. However, these approaches are not generic, as they focus towards a particular plane and cannot adapt to handle abnormal, irregular and/or partial images. Further, these approaches may not work well on real-world data. For example, the variability across patients can be quite large, where many seemingly plausible heuristics would fail. Additionally, diseases or artifacts can alter/fade out a particular anatomy. Furthermore, a partial field of view can lead to partial data problems.
Thus, there is a need for a detection system and method that is robust and generic to different variations, adaptable to different applications/problems and automatic to save time and improve consistency/repeatability, for example, in follow-up studies of the same patient or a cross-patient comparison.